Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform

Abstract

Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers.

Methods and materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt).

Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers.

Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

Figure 1. Defining the pathway scale by mathematical modeling

A linear regression algorithm is used to model the pathway/network scale in the radiosensitivity continuum. Biological variables (ras status, p53 status and TO) known to influence radiosensitivity along with gene expression are included in the model

Figure 2. The radiosensitivity network

GeneGO™ MetaCore™ was used to generate a network of direct connections between the 500 genes selected for analysis. Red, green and gray arrows indicate negative, positive and unspecified effects.

Figure 3. A hub-based network view of the radiosensitivity model

Hubs were identified as having more than 5 connections within the network. STAT1, IRF1, NFKB, AR, and c-Jun are indicated as transcription factors while HDAC1, CDK1, PKC and c-Abl are annotated as enzymes. SUMO1 is annotated as a protein.

Figure 4. Integration of biological parameters in the gene expression/SF2 model

500 gene-based linear models of radiosensitivity were clustered based on the impact of each term within the model. Each spot in the heatmap represents a p-value from a single coefficient within each individual gene-based model. Terms include gene expression (y), TO (tissueType), ras status (RASmut) and p53 status (P53mut). The combination of two terms (e.g. y:RASmut) indicates an interaction term.

Figure 5. Biological validation of c-Jun

A. c-Jun was knocked down in eight cell lines using siRNA and SF2 was determined. The mean and standard errors from at least five independent experiments in triplicates are represented. Down-regulation of c-Jun was verified by Western blot.B. C-Jun gene expression is directly proportional to radiosensitivity in lung cancer cell lines. Graphic representation of c-Jun gene expression and SF2 in lung cancer cell lines in the 48 cell line dataset (A549, H460, HOP62, NCIH23, HOP92, EKVX).